The future brings uncertainty. Predicting what will happen in a given situation—or multiple situations—is often difficult, especially when there is a wide margin for human and natural error. This is particularly challenging in financial decision-making, such as investment or environmental planning.

To reduce this error, Akhil Langer is developing a parallel computing framework to simplify the decision-making process by incorporating future uncertainty to make decisions that, for example, maximize profits and minimize expenses. Problems arise when attempting large-scale decision-making on a single computer, because there are too many possible scenarios to process. This is why Akhil is working on parallel solutions that can exploit tens of thousands of computers to solve large optimization problems.

One application is the problem of aircraft allocation for the U.S. Department of Defense. The Department of Defense must allocate 1,300 U.S. military aircrafts on different missions—a task it presently assigns manually. Akhil’s goal is to optimize aircraft allocation using parallel computing on some of the most powerful supercomputers.

As an undergraduate, Akhil previously worked on a project that involved the distribution of health-related information to users through their cell phones. Users could ask questions, such as how to locate a doctor, or request information on a certain drug via informal text messages. Akhil's software would then analyze their queries and return appropriate data from a health database to answer their questions.

Akhil received his undergraduate degree in Computer Science from the Indian Institute of Technology Roorkee and holds a Master's degree in Computer Science from the University of Illinois at Urbana-Champaign.

The human genome sequence contains over three billion letters. Understanding what the vast majority of these letters encode for has long been a mystery. Emerging technologies are now providing a variety of data on the human genome, but can produce tens of millions of data points—and that’s where computational biologists step in.

Jason Ernst uses computational methodologies to integrate different data sources to analyze the human genome with the ultimate goal of better understanding and treating disease. He develops and applies computational methods to the data, which can then be used to provide insights into different cell phenotypes and disease-associated DNA variations.

Jason was originally trained as a computer scientist, but became motivated by questions in biology, while a Ph.D student at Carnegie Mellon University studying machine learning.

The technology and data resulting from the Human Genome Project prompted Jason to recognize that this was a situation in which he could apply machine learning. He enjoys the computational challenges and opportunity to collaborate with experimental biologists, and hopes genomic work will eventually have a greater effect on personalized medicine and human health.

Jason was a postdoctoral fellow at MIT during which time he was affiliated with the Broad Institute—a genomic medicine research center in Cambridge, Massachusetts. He holds his undergraduate degrees in Computer Science and Mathematics from the University of Maryland, College Park, and is currently an Assistant Professor at the University of California, Los Angeles. In his spare time, he enjoys running, Ultimate Frisbee, and soccer.

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Rayid GhaniCarnegie Mellon, Computer Science, Class of 2001

The New York Times has hailed Rayid Ghani as Obama’s “secret engine” for re-election. As Chief Data Scientist, Rayid Ghani invented algorithms to help target voters about the presidential candidates. Ghani and the analytics team broke down the goal of 270 electoral votes into problem sets to answer questions like: who are the swing voters, how do you target each swing voter specifically and how do you mobilize your voters to the voting booth on Election Day? His team’s work resulted in a list of tens of millions of targeted names and a strategy to optimize their volunteers and funds in the most efficient and effective way. He targeted young voters by encouraging them to sign into the Obama campaign website through their facebook accounts and accessed their social networks to identify persuadable friends. He then encouraged Obama voters to share their Obama pitch with their ten most persuadable facebook friends.

Before joining up with the campaign, Ghani never thought of working in the political arena. Without a set plan, Ghani left Accenture Labs after 10 years as a Senior Research Scientist and Director of Analytics Research seeking a fresh opportunity where he could have big social impact. He had no idea how big until a few connections in Chicago recruited him for the position of Chief Data Scientist for Obama’s presidential re-election campaign. At a basic technical level, the data gathering, analyzing, and conclusion process was similar to his work at Accenture Labs, but the steep ramp up of the campaign was unlike anything he had experienced before. Within a year and a half, he helped build a team of workers and volunteers that grew exponentially, the constant organization of which became one of the hardest challenges. It was a significant commitment, with long hours—up to 20 hours a day, 7 days a week at the final push--with an aggressive deadline where failure meant national, even global, repercussions. Conversely, seeing everyone coming together, sacrificing time and effort for one committed cause also became Ghani’s greatest inspiration.

Currently, Ghani and some campaign colleagues are modifying the data analysis tools they used in the campaign to help nonprofits. As Ghani indicates, nonprofits collect the data, but lack the resources to take advantage of the useful conclusions that can be gathered through analysis. He hopes that he and his colleagues can create better resources for non-profits to utilize their volunteers, find more volunteers, and magnify their outreach, which will in turn help them make the most of their funds. Rayid has also joined the University of Chicago at the Computation Institute and Public Policy School to work at the intersection of analytics and high-impact social problems.

Ghani received his M.S. in Knowledge Discovery & Data Mining at Carnegie Mellon in 2001. He has over 50 academic publications, 15 patents filed (seven awarded so far), and 2000 citations in journals, conferences, and workshops. His work has been highlighted by Time, The New York Times, Slate, Business Week, Financial Times, Chicago Tribune, US News & World Report, andNBC.

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Amy ChenStanford University, Business, Class of 2007

Almost 20 million children across the United States rely on subsidized free or reduced-price lunch programs as one of their main sources of nutrition during the school year. But during the summer, many of these children do not have safe, reliable transportation options to the locations where free meals are offered.

Taking a cue from the neighborhood ice cream truck, Amy Chen and her team piloted a program through PepsiCo’s Food for Good initiative to bring healthy meals directly to children in need in South Dallas. The program, which is a partnership between PepsiCo, local community organizations, and the government, has since expanded to other locations in Dallas as well as Chicago and now serves over 300,000 meals each summer.

The 1.5-year-old Food for Good program is focused on using business to solve social problems, working with inner-city communities to address their specific challenges with impactful, sustainable solutions. For example, many of the areas served by Food for Good lack healthy food options, as grocery stores are not located nearby and local convenience stores do not offer affordable, nutritious food or fresh fruits and vegetables. Amy and her team are exploring and piloting a number of new initiatives to address this systemic challenge, including an urban teaching farm in partnership with a local college and community-run farm stands offering produce centrally within the community.

As Project Manager for Food for Good, Amy has her dream job blending her business and policy experience with her passion for social justice. She holds a J.D./MBA degree from Stanford University as well as a Bachelor’s degree in Chemistry from Harvard University.

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Jason HongUC Berkeley, Computer Science, Class of 2004

When Associate Professor Jason Hong’s blackjack app on his smartphone asked him for his location, he wondered what his location had to do with a poker game and debated giving the app what it wanted. His curiosity led to an investigation to find out which other apps access personal user information. He took his experiment a step further by presenting his findings to users and gauging their reaction and level of awareness.

Jason’s research group at Carnegie Mellon University specializes in human computer interaction and has studied user privacy and security issues for a decade. Jason and his team discovered that the most unsuspecting apps, like Angry Birds or the Brightest Flashlight app, access sensitive data such as our contact lists, unique device id and location. When confronted with this data, most users were shocked and even disturbed to discover the personal information these apps access, causing many to delete the apps. In our technological age, the more a company knows about its users, the better it can advertise to their needs, sometimes at the cost of the user’s privacy. As Jason indicates, technology can only enhance our lives if we use it, not if we are suspicious and avoidant due to privacy concerns. Technology can only maintain the trust of the user by meeting privacy and security standards, which as of now are virtually nonexistent when it comes to smartphone apps.

Jason hopes that his research will spread user awareness, lead developers to create better interface for apps and inspire new privacy and security laws to protect smartphone users. Next up, Jason plans to study the human behavior trends of cities in real time to compile useful data for urban planners, politicians and sociologists, such as, what happens to neighboring businesses when a Target opens or how far will people travel to shop at the only organic grocery store in town?

Jason received his Ph.D. from the University of California, Berkeley and his undergraduate degree from Georgia Institute of Technology. Jason is co-founder of Wombat Security Technologies, is an Alfred P. Sloan Foundation Fellow and a Kavli Fellow, and has participated on DARPA's Computer Science Study Panel (CS2P).

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Get to know our featured Scholars. Click the images to learn more.

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Ms. Sara Metcalf

Education & Work

School

MIT Sloan

Class

2001

Area of Study

Business

Current Employer

State University of New York (SUNY) at Buffalo

Current Title

Assistant Professor of Geography

Professional Interests & Experience

Bio

As a faculty member in the State University of New York at Buffalo Geography Department, I study urban geography and practice dynamic modeling while teaching courses in both. Current research interests are the role of urban agriculture in local food systems and the social dimensions of healthy aging in urban environments. I employ the methodology of system dynamics in constructing, simulating, and testing stock-flow and agent-based models of resource issues, migration patterns, and other aspects of human interactions in the urban context.